NESUG 2012 PROCEEDINGS
Programming: Beyond the Basics Robert Schechter, PharmaNet/i3 Parag Shiralkar, eClinical Solutions This section will present abstract submissions that address a broad spectrum of advanced SASR topics, including ODS, Macro, PROC Report, SQL, JMP, and sophisticated, efficient DATA step and PROC step programming. Other objectives of this section include presentation of new features available as a part of SAS 9.2 and SAS 9.3 versions and innovative problem solving techniques using SAS applications. Topics include, but are not limited to, the use of SAS components to address advanced analytical, reporting, data mining, and data management applications. The objective of these tutorial-‐style presentations is to provide a deeper, practical understanding of key SAS features and behaviors that can make the attendee a more efficient, valuable SAS programmer. Interesting Technical Mini-‐Bytes of Base SAS®—From DATA Step to Macros
Airaha Chelvakkanthan Manickam, Cognizant Technology Solutions
BB1
Loading Metadata to the IRS Compliance Data Warehouse (CDW) Website: From Spreadsheet to Database Using SAS® Macros and PROC SQL
Robin Rappaport, Internal Revenue Service -‐ Research, Analysis, and Statistics Jeff Butler, Internal Revenue Service -‐ Research, Analysis, and Statistics
BB2
Using Axes Options to Stretch the Limits of SAS/GRAPH® Template Language
Perry Watts, Stakana Analytics
BB4
Automatically Generating SAS® Code from Client Specifications
Stanley Legum, Westat
BB5
NESUG 2012 PROCEEDINGS
An Efficient Method to Create a Large and Comprehensive Codebook
Wen Song, ICF International Kamya Khanna, ICF International Baibai Chen, ICF International
BB6
Some _FILE_ Magic
Mike Zdeb, U@Albany School of Public Health
BB7
Data Validation and Transformation in ETL Processing with Help of Perl Regular Expressions in SAS®
Val Volovik, Alliant, LLC
BB8
Dynamic Data Processing Using Data-‐Driven Formats and Informats
Jedediah Teres, MDRC
BB9
SAS® and R Working Together
Matthew Cohen, Wharton School
BB10
There’s an App for That: It’s Called SAS® ODS! Mobile Data Entry and Reporting via SAS ODS
Michael Drutar, SAS Institute Inc.
BB11
Using PROC TABULATE and ODS Style Options to Make Really Great Tables
Wendi Wright, CTB McGraw-‐Hill
BB12
A Paperless Report Generation and Distribution System
George Sharrard, GPS Corp
BB13
NESUG 2012 PROCEEDINGS
Use the Full Power of SAS® in Your Function-‐Style Macros
Mike Rhoads, Westat
BB14
Adding Control to Your Data Using SAS® System and Dataset Options
David Franklin, TheProgrammersCabin.com
BB15
Publishing SAS® Metadata Using Macros, PROC SQL and Dictionary Tables
John Fahey, Reproductive Care Program of Nova Scotia Barry Campbell, Reproductive Care Program of Nova Scotia
BB16
An Introduction to Perl Regular Expressions in SAS® 9
Selvaratnam Sridharma, U.S. Census Bureau
BB17
Rounding Up the Stragglers: Dynamically Detecting and Processing All Existing Data Sets
Adam Miller, Management Science Associates, Inc.
BB18
NESUG 2012 PROCEEDINGS
Coders' Corner Claudine Lougee, Dualenic, LLC Christopher Battiston, Hospital for Sick Children Coders' Corner includes brief presentations to give presenters an opportunity to show off their "magic" skills or "trick" code in a fast paced or demo-‐like environment. The presentations, which are only 10 minutes in length, allow for a snapshot of the topic giving novices to experts a flavor for different aspects of SAS®. Topics can range from ODS output to PROC SQL to SAS/Graph to Enterprise Guide or any programming skills related to using SAS. This section gives plenty of opportunity for any user to walk away with practical, useful information that can be applied immediately. Kick It Old School—Creating Reports with the DATA _NULL_ Step
Sai Ma, PharmaNet/i3 Suwen Li, Everest Clinical Research Services Inc. Minlan Li, Everest Clinical Research Services Inc.
CC1
Not Dividing by Zero: Last of the Low-‐Hanging Efficiency Fruit
Bruce Gilsen, Federal Reserve Board
CC2
With a Trace: Making Procedural Output and ODS Output Objects Work For You
Louise Hadden, Abt Associates Inc.
CC3
Penetrating the Matrix
Justin Smith, U.S. Census Bureau William Zupko, U.S. Census Bureau
CC4
Exploring the PROC SQL _METHOD Option
Kirk Paul Lafler, Software Intelligence Corporation
CC6
Checking Out Your Dates with SAS®
Christopher Bost, MDRC
CC7
NESUG 2012 PROCEEDINGS
Writing Flexible SAS® Codes: Exploring the Value of Global Macro Variables, Conditional Statements, and %SYSFUNC
Victoria Porterfield, Rutgers University
CC8
Accessing SAS® Code via Visual Basic for Applications
Jennifer Davies, Z, Inc
CC9
A Practical Application of PROC GPLOT and PROC GCHART and Annotate to Clinical Trial Data
Tulin Shekar, Merck & Co., Inc.
CC10
Checking for Duplicates
Wendi Wright, CTB McGraw-‐Hill
CC11
Using PROC FCMP to Solve Rolling Regression Rapidly
Chao Huang, Oklahoma State University Liang Xie
CC12
Using SAS® to E-‐Mail Reports and Results to Users
Stuart Summers, Alliant, LLC
CC13
Using PROC SQL and the SAS® Macro Facility to Blind Formatted Dates on a Group of Datasets within a Single Directory
Diana Ventura, Harvard University
CC14
Store and Manage Routinely Extracted Observations: A Practical Application
Si Gao, State University of New York -‐ Albany Xin Li, State University of New York -‐ Albany
CC16
NESUG 2012 PROCEEDINGS
Create User-‐Defined Formats: Using SAS® Code to Write Code
Yonghong Shang, Westat
CC17
Correcting for Natural Time Lag Bias among Non-‐Participants in Pre-‐Post Intervention Evaluation Studies
Gandhi Bhattarai, UnitedHealth Group
CC18
The SAS® System Generates Code for You While Using File IMPORT and EXPORT Procedures
Anjan Matlapudi, Amerihealth
CC20
Careful Use of Input Delimiters
Howard Schreier, Howles Informatics
CC23
Managing SAS® Dates with Irregular Granularity
Howard Schreier, Howles Informatics
CC24
You Have SASMAIL!
Rajbir Chadha, Cognizant Technology Solutions
CC25
Multitasking with Nested Formats
Jonathan Kerman, Johns Hopkins Bloomberg School of Public Health
CC26
A Macro to Squeeze SAS® Data and Create a SAS Transport File
Hany Aboutaleb, Biogen Idec
CC27
Mimicking the DATA Step Dash and Double Dash in PROC SQL
Arlene Amodeo, Law School Admission Council
CC28
NESUG 2012 PROCEEDINGS
Using SAS® to Control the Postprocessing of MS Documents
Nat Wooding, J. Sargeant Reynolds Community College
CC29
Yes, No, Maybe So: Tips and Tricks for Using 0/1 Binary Variables
Laurie Hamilton, Healthcare Management Solutions LLC
CC30
%ASSERT Your Way to Sleep-‐Filled Nights: A One-‐Line Macro for Data Validation
Quentin McMullen, Siemens Healthcare
CC31
Calculating Questionnaire Scores Made Easy in SAS®
Qin Lin, ACI, LLC
CC32
Analyze and Manage Your Output with PROC MEANS
Si Gao, State University of New York -‐ Albany Xin Li, State University of New York -‐ Albany
CC33
Fun with PROC SQL
Darryl Putnam, CACI, Inc.
CC34
Summing Data with the SUM Function in SAS®
Anjan Matlapudi, AmeriHealth Daniel Knapp, AmeriHealth
CC35
Sorting a Large Data Set When Space is Limited
Selvaratnam Sridharma, U.S. Census Bureau
CC36
Bring Excel Files with Multiple Sheets to SAS®
Mindy Wang, Marriott International
CC37
NESUG 2012 PROCEEDINGS
Seek and Ye Shall FINDC: A Powerful Function for Qualitative Data Validation
Xiaoke Yang, State University of New York -‐ Albany Peter Landi, New York State Office of Mental Health
CC38
Using PROC TCALIS to Investigate Equality of Covariance Matrices across Two Groups
Chen Li, Educational Testing Service Steven Holtzman, Educational Testing Service
CC39
Combining Continuous Events and Calculating Duration in Kaplan-‐Meier Analysis Using a Single DATA Step
Hui Song, PRA International Inc. George Laskaris, PRA International Inc.
CC40
NESUG 2012 PROCEEDINGS
Programming: Foundations and Fundamentals Jonas V. Bilenas, Barclays UK RBB Stan Legum, Westat
This section includes presentations that focus on a wide range of beginning and intermediate SAS® topics. The papers focus on data step and macro programming. Data step examples include the most useful SAS functions; interfacing with Microsoft Office products, particularly Excel; creating reports; and accessing data from different sources, In addition there are presentations on Enterprise Guide and the use of key PROCs. The presentations should provide beginning and intermediate users with a richer knowledge of SAS programming and increase their productivity in using SAS.
Long-‐to-‐Wide: PROC TRANSPOSE vs Arrays vs PROC SUMMARY
Mike Zdeb, U@Albany School of Public Health
FF1
Let SAS/SHARE® Deliver Formatted Data to Microsoft Office
Hsiwei Yu, Custom Software Systems Tao Dong, Pragmatics
FF2
A Survey of Some of the Most Useful SAS® Functions
Ron Cody
FF3
Loop-‐Do-‐Loop Around Arrays
Wendi Wright, CTB McGraw-‐Hill
FF4
Creating a Data Dictionary for an Oracle Database Using SAS® 9.3
Christopher Battiston, Hospital for Sick Children
FF6
Rediscovering the DATA _NULL_ for Creating a Report, and Putting That Text File into RTF in a Single DATA Step
David Franklin, TheProgrammersCabin.com
FF7
NESUG 2012 PROCEEDINGS
Imputing Endpoints after Collapsing Longitudinal Data across Related Events
James Joseph, INC Research
FF8
How to Monitor Production and Development Processing Using the SAS® Logging Facility
Curtis Reid, U.S. Bureau of Labor Statistics
FF9
Integrating SAS® and Excel: An Overview and Comparison of Three Methods for Using SAS to Create and Access Data in Excel
Nathan Clausen, U.S. Bureau of Labor Statistics Edmond Cheng, U.S. Bureau of Labor Statistics
FF10
You Want ME to use SAS® Enterprise Guide®??
Vince DelGobbo, SAS Institute Inc.
FF11
NESUG 2012 PROCEEDINGS
Finance and Insurance Mark Keintz, Wharton Research Data Services Barbara Moss, The Hartford Finance and insurance-‐related applications and research constitute the second largest customer segment of SAS users. This year's section demonstrates the use of SAS in such areas as financial impacts on Medicare providers using simulation analysis, creating finite mixture models, identification of leading and lagging indicators, finding voided claims records, and rolling regressions. Presentations include examples of "classic" SAS modules such as SQL and LIFETEST through more recent tools such as Finite-‐Mixture modeling, text analysis, and PROC FCMP. Remove Voided Claims for Insurance Data
Qiling Shi, NCI Information Systems, Inc.
FI2
Leading and Lagging Indicators in SAS®
David Corliss, Magnify – a division of Marketing Associates
FI3
Modeling Loss Given Default by Finite Mixture Model
Chao Huang, Oklahoma State University Liang Xie
FI4
Becoming the Smartest Guys in the Room: An Analysis of the Enron Emails Using an Integration of Text Analytics and Case Management
John York, SAS Institute Inc.
FI6
Financial Impact Analysis of the New RUG-‐IV on Post-‐Acute Medicare Providers Using Monte Carlo Simulation
John Gao, PointRight Cheryl Caswells, PointRight Barry Fogel, PointRight
FI7
Rolling Regressions with PROC FCMP and PROC REG
Mark Keintz, Wharton Research Data Services
FI8
NESUG 2012 PROCEEDINGS
Graphics and Reporting Perry Watts, Stakana Analytics Darcy Tamburri, NECA Transforming descriptive data from SAS into informative, attractive tabular and graphics output is a requirement for today’s business analyst. The Graphics and Reporting section will present various uses of SAS tools to effectively create professional looking graphs and reports. Presentations include the use of SAS Graph procedures, specifically GEOCODE, GCHART, GPLOT, G3D, GREPLAY and GMAP as well as the Graph Template Language and Statistical Graphics procedures. The use of multiple ODS output destinations, ODS Graphics and PROC REPORT will also be demonstrated. Behind the Scenes with SAS®: Using Custom Graphics in SAS Output
Louise Hadden, Abt Associates Inc.
GR1
Analyzing the Safewalk® Program with SAS®: Saving Shelter Dogs One Walk at a Time
Louise Hadden, Abt Associates Inc. Terri Bright, Massachusetts Society for the Prevention of Cruelty to Animals
GR2
ODS DOCUMENT Step by Step
Wendi Wright, CTB McGraw-‐Hill
GR3
Destination Known: Programmatically Controlling Your Output in SAS® Enterprise Guide®
Aaron Hill, MDRC
GR4
Graphics for Univariate Data: Pie is Delicious But Not Nutritious
Peter Flom, Peter Flom Consulting
GR6
NESUG 2012 PROCEEDINGS
Scatter Plot Smoothing Using PROC LOESS and Restricted Cubic Splines
Jonas Bilenas, Barclays UK E RBB
GR7
He Who Graphs SAS® Graphs Best
Lisa Aronson Friedman, Johns Hopkins Bloomberg School of Public Health
GR8
A Map is Just a Graph Without Axes
Nat Wooding, J. Sargeant Reynolds Community College
GR9
Virginia’s Best: How to Annotate County Names and Values on a State Map
Anastasiya Osborne, Farm Service Agency, USDA
GR10
Off the Beaten Path: Create Unusual Graphs with ODS Graphics
Prashant Hebbar, SAS Institute Inc.
GR12
NESUG 2012 PROCEEDINGS
Hands-‐On Workshops Dalia Kahane, Westat Anastasiya Osborne, Farm Service Agency, USDA Hands-‐On Workshops allow attendees to reinforce their understanding of presentation content by following the instructor through exercises and examples on a workshop computer. Workshop topics include presentations on Exporting SAS data to Excel, ODS, SQL, SAS graphics, SAS macros, data manipulation methods, and more. The target audience is attendees at both the beginning and intermediate levels. A Tutorial on the SAS® Macro Language
John Cohen, Advanced Data Concepts, LLC
HW1
Quick Results with Output Delivery System (ODS)
Kirk Paul Lafler, Software Intelligence Corporation
HW2
PROC SQL for DATA Step Die-‐Hards
Christianna Williams, self-‐employed
HW3
Using SAS® ODS Graphics
Chuck Kincaid, Experis Business Analytics
HW4
Quick Results with PROC SQL
Kirk Paul Lafler, Software Intelligence Corporation
HW5
An Introduction to Creating Multi-‐Sheet Microsoft Excel Workbooks the Easy Way with SAS®
Vince DelGobbo, SAS Institute Inc.
HW6
SAS® Enterprise Guide®: Finally, a Programmer’s Tool
Marje Fecht, Prowerk Consulting Rupinder Dhillon, Dhillon Consulting
HW7
NESUG 2012 PROCEEDINGS
Large Data Sets Sara Hickson, Harvard Medical School Sandeep Kottam, Independent Consultant Some programming practices that cause no real problems when working with smaller data sets can generate significant obstacles when working with large data sets. These may include, but are not limited to: ·∙ Excessive I/O ·∙ Extensive and high-‐maintenance scripts ·∙ Network access and throughput constraints ·∙ Inefficient retrieval of subsets ·∙ Sparse data
The NESUG 2012 section on Large Data Sets will include papers that describe methods to minimize problems that occur when processing large amounts of data. Presentations may offer solutions that range from those that are very simple to advanced, involving techniques such as macros, data organization and custom indexing.
Using FORMATs Where SQL Won’t Do
James Zeitler, Harvard Business School
LD1
Top Ten SAS® Performance Tuning Techniques
Kirk Paul Lafler, Software Intelligence Corporation
LD2
Standardized Production Processes for One Routine Government Publication—Using SAS® 9.2 to Produce Mortality Tables for the National Center for Health Statistics’ Annual Report, “Health, United States”
Mary Ann Bush, Centers for Disease Control and Prevention, National Center For Health Statistics Sheila Franco, Centers for Disease Control and Prevention, National Center For Health Statistics Li-‐Hui Chen, Centers for Disease Control and Prevention, National Center For Health Statistics Shilpa Bengeri, NOVA Research
LD3
NESUG 2012 PROCEEDINGS
Using SAS® Enterprise Guide® to Handle Backend Data Preparation for the Health Indicators Warehouse
Li-‐Hui Chen, Centers for Disease Control and Prevention, National Center For Health Statistics Mary Ann Bush, Centers for Disease Control and Prevention, National Center For Health Statistics Kate Brett, Centers for Disease Control and Prevention, National Center For Health Statistics
LD4
Tips for Using SAS® to Manipulate Large-‐scale Data in Databases
Shih-‐Ching Wu, Virginia Tech Transportation Institute Shane McLaughlin, Virginia Tech Transportation Institute
LD5
Condensed and Sparse Indexes for Sorted SAS® Datasets
Mark Keintz, Wharton Research Data Services
LD6
NESUG 2012 PROCEEDINGS
Management, Administration and Support Mary Anne Rutkowski, Merck Sharp and Dohme Corp. Kathy Harkins, Merck Sharp and Dohme Corp. The Management, Administration & Support section covers a wide range of topics focused on best practices for increasing productivity from employees, processes, projects, and products related to the use of SAS® Software. In the current professional environment, employees must sustain high productivity and efficiency while managers must adapt to managing in an ever-‐ changing environment. Today’s evolving business models must respond to increased globalization, standardization, accountability, out-‐sourcing, telecommuting, and electronic collaboration. To cope with the current environment, SAS® users must continue to develop and expand their skills and grow professionally. Topics in the Management, Administration & Support section can cover various areas such as optimizing operations in an evolving business environment with quality assurance, effective utilization of SAS for administration and management of data, management techniques, SAS infrastructure maintenance and other administrative facilities. SAS® UNIX-‐Space Analyzer—A Handy Tool for UNIX SAS Administrators
Airaha Chelvakkanthan Manickam, Cognizant Technology Solutions
MA1
Better Safe than Sorry: A SAS® Macro to Selectively Back Up Files
Jia Wang, Data and Analytic Solutions, Inc. Zhengyi Fang, Social & Scientific Systems, Inc.
MA2
Step by Step Approach to Port CDISC SAS® Data Integration Repositories on Cross Platforms Using %OMAPORT Macro
Salman Ali, SAS Professional Services
MA3
Creating an Interactive SAS® Textbook in the iPad with iBooks Author
William Zupko, U.S. Census Bureau
MA4
NESUG 2012 PROCEEDINGS
Quality Assurance: Best Practices in Clinical SAS® Programming
Parag Shiralkar, eClinical Solutions, a Division of Eliassen Group
MA5
Process Simplification—The Simple Way! X
Minal Vyas, Hoffmann-‐La-‐Roche
MA6
PROC CPM and PROC GANTT: The Next Step in Multi-‐Project Management
Stephen Sloan, Accenture Lindsey Puryear, SAS Institute
MA7
Here Comes Your File! File-‐Watcher Tool with Automated SAS® Program Trigger
Rajbir Chadha, Cognizant Technology Solutions
MA8
The Disk Detective
Darryl Putnam, CACI, INC
MA9
Determining What SAS® Version and Components Are Available
David Chapman, Chapman Analytics, LLC
MA10
Running SAS® on the Grid
Margaret Crevar, SAS Institute Inc.
MA11
NESUG 2012 PROCEEDINGS
Pharma, Healthcare and Life Sciences John Cohen, Advanced Data Concepts LLC Emmy Pahmer, PharmaNet/i3 This section will encompass the use of SAS® in all life science domains including pharmaceutical research, health care delivery, and health insurance reimbursement. The focus will be on programming for auditing, analyzing and reporting, and system/application development, rather than on statistics. Papers will cover topics such as applying SAS to the challenges of the pharmaceutical industry, understanding issues related to regulatory compliance, and exploring emerging industry standards like CDISC, SDTM, and ADaM. Data may be clinical, post-‐marketing or related to commercial sales and marketing. Also covered are health-‐care provider metrics to address quality-‐of-‐care issues, outcomes, pay for performance, provider efficiencies, profitability, and health economics.
Generating Estimates for U.S. Healthcare Costs and Use
Paul Gorrell, IMPAQ International, LLC
PH1
Pharma Sales Reporting Using SAS® Enterprise Guide® 4.3—A Step-‐by-‐Step Practical Approach
Airaha Chelvakkanthan Manickam, Cognizant Technology Solutions Ramya Purushothaman, Cognizant Technology Solutions
PH2
Prove QC Quality—Create SAS® Datasets from RTF Files
Honghua Chen, OCKHAM
PH3
Patient Profiles “On the Cheap”: Quickly Capitalizing on PROC SQL and PROC REPORT to Efficiently and Cost Effectively Create Patient Profiles for Sponsor Source Data Verification, Audit, and Other Types of Clinical Data Verifications and Reviews
Mark Rothe, Roche
PH4
Length of Intensive Care Unit Stay Computed from the VA Corporate Data Warehouse
Adeline Wilcox, Department of Veterans Affairs
PH6
NESUG 2012 PROCEEDINGS
Data Rolls Up-‐Hill: Reverse Waterfall Provider Analysis Using SAS®
John Busch, Community Care Behavioral Health
PH8
Programming to Stay Afloat in Data Waves
Gayle Springer, Johns Hopkins Bloomberg School of Public Health Lorie Benning, Johns Hopkins Bloomberg School of Public Health
PH9
Applying Business Analytics to Optimize Clinical Research Operations
David Handelsman, SAS Institute Inc.
PH10
Harnessing the Power of SAS® ISO 8601 Informats, Formats, and the CALL IS8601_CONVERT Routine
Kim Wilson, SAS Institute Inc.
PH11
NESUG 2012 PROCEEDINGS
Posters Louise Hadden, Abt Associates Inc. Nish Herat, Independent SAS Consultant
The Posters section entertains a rich and varied collection of papers filled with comprehensive idea-‐generating solutions and highly specialized papers that can easily be expanded to your world. Through the "Meet the Poster Presenter" session Monday morning you will have access to paper authors and their work in a less formal, personable setting. Tackle large, complex databases with Hash, get to truly dynamic programming with SAS/SCL, see what SAS can do with the persistent problem of visualizing vast quantities of data, or how the US Geological Survey's internet available data can be used to populate maps with earthquake data. In addition, wise programming techniques, tips from SAS Tip of the Day, and easy ways to get useful rows of zeroes in your data are all on offer. And of course, if you prefer clicks to semicolons, Enterprise Guide and the overlooked Graph'n'Go will be represented so everyone can enjoy the views from the hotel atrium and learn something unexpected.
A SAS® Tip-‐of-‐the-‐Day Web Page on an Intranet
Bruce Gilsen, Federal Reserve Board
PO1
A Breeze through SAS® Options to Enter a Zero-‐Filled Row
Kajal Tahiliani, ICON Clinical Research
PO3
Decision-‐Making Using the Analytic Hierarchy Process (AHP) and SAS/IML®
Melvin Alexander, Social Security Administration
PO4
Working with a Large Pharmacy Database: Hash and Conquer
David Izrael, Abt Associates
PO5
NESUG 2012 PROCEEDINGS
A Visual Approach to Monitoring Case Report Form Submission During Clinical Trials
Rebecca Horney, Dept. of Veterans Affairs Cooperative Studies Program Coordinating Center Karen Jones, Dept. of Veterans Affairs Cooperative Studies Program Coordinating Center Annette Wiseman, Dept. of Veterans Affairs Cooperative Studies Program Coordinating Center
PO6
Using SAS/OR® for Automated Test Assembly from IRT-‐Based Item Banks
Yung-‐chen Hsu, GED Testing Service, LLC Tsung-‐hsun Tsai, Research League, LLC
PO7
Simple Statistical Programming: Preventing Errors When Creating Output Datasets Containing Statistical Test Results for McNemar’s Test
Stephen Bosch, Howard M. Proskin & Associates
PO8
Practical Application of SAS® Capabilities for Pharma Goaling and Performance Review
Ramya Purushothaman, Cognizant Technology Solutions
PO9
Wake Up Your Data with Graph’n’Go
Christopher Battiston, Hospital for Sick Children
PO10
Avoiding the “Ooh Nasty”
David Franklin, TheProgrammersCabin.com
PO11
NESUG 2012 PROCEEDINGS
A SAS® Program to Construct Simultaneous Confidence Intervals of Relative Risk for Multiple Adverse Events
Xiaoli Lu, Dept. of Veterans Affairs Cooperative Studies Program Coordinating Center
PO12
Plotting Earthquake Events Using the SAS® PROC GMAP and Data from the USGS
David Franklin, TheProgrammersCabin.com
PO13
Using SAS® GTL with 9.3 Updates to Visualize Data When There is Too Much of it to Visualize
Perry Watts, Stakana Analytics Nate Derby, Stakana Analytics
PO14
Using SAS® SCL to Create Flexible Programs... A Super-‐Sized Macro
Ellen Michaliszyn, College of American Pathologists
PO15
NESUG 2012 PROCEEDINGS
Statistics, Modeling and Analysis George J. Hurley, The Hershey Company Peter Flom, Peter Flom Consulting, LLC
The Statistics, Modeling and Analysis section includes abstract submissions that cover the use of SAS® in applied statistical, analytical, epidemiological, and survey methods across a variety of industries, such as consumer packaged goods, health care and pharmaceuticals, government, marketing, and a variety of academic fields. Papers can illustrate business or research problems. Most papers illustrate approaches to satisfying a research or business need, including an analytic or statistical method for addressing the need and demonstrating a SAS and/or JMP implementation of that method.
K-‐Nearest Neighbor Classification and Regression in SAS®
Liang Xie, Travelers Insurance
SA1
Creating and Displaying an Econometric Model Automatically
William Zupko, U.S. Census Bureau Justin Smith, U.S. Census Bureau
SA3
SAS® for Six Sigma—An Introduction
Dan Bretheim, Towers Watson
SA4
An Animated Guide: Regression Trees in JMP® and Enterprise Miner ™
Russ Lavery, Contractor
SA5
Elongated Intersected Clusters and Radial Coordinates Transformation Using SAS®
Alexander Suprun, CIBC
SA6
Target Trained Transformations for Predictive Modeling
Talbot Katz, Cisco Systems, Inc.
SA7
NESUG 2012 PROCEEDINGS
Using SAS® to Test, Probe and Display Interaction Effects in Regression
Timothy Gravelle, PriceMetrix Inc.
SA8
Efficiently Screening Predictor Variables for Logistic Models
Steven Raimi, Magnify – a division of Marketing Associates, LLC Bruce Lund, Magnify – a division of Marketing Associates, LLC
SA9
Developing an Analytics Center of Excellence (Or the Care and Feeding of Magical Creatures)
Chuck Kincaid, Experis Business Analytics
SA10
Introduction to Statistics with Wavelets in SAS®
David Corliss, Magnify – a division of Marketing Associates
SA11
Implementing and Interpreting Canonical Correspondence Analysis in SAS®
Laxman Hegde, Frostburg State University
SA12
Applying Customer Attitudinal Segmentation to Improve Marketing Campaigns
Wenhong Wang, Deluxe Corporation Mark Antiel, Deluxe Corporation
SA13
Profiling Consumer Price Index Formulas
Joshua Klick, U.S. Bureau of Labor Statistics
SA14
A Macro-‐Driven Approach for Systematically Testing Variables Against a Base Regression Model
Jennifer Alessandro, Management Science Associates, Inc. Bryan Harmon, Management Science Associates, Inc.
SA15
NESUG 2012 PROCEEDINGS
Generalized Additive Models in Marketing Mix Modeling Revisited
Patralekha Bhattacharya, Jigyasa Analytics
SA16
A Model for Extreme Stacking of Data at Endpoints of a Distribution
Robert Gallop, West Chester University
SA17
What?? SAS/AF® & SCL Can Enhance Rapid Prototyping of Modern Web Development Efforts?
Joe Whitehurst, High Impact Technologies Richard Devenezia, High Impact Technologies Art Tabachneck, myQNA
SA18
Look Out: After SAS/STAT® 9.3 Comes SAS/STAT 12.1!
Maura Stokes, SAS Institute Inc.
SA19
Introducing the FMM Procedure for Finite Mixture Models
Maura Stokes, SAS Institute Inc.
SA21
Building a Predictive Model for 30-‐Day Inpatient Readmission Using PROC PHREG
Klaus Lemke, Johns Hopkins Bloomberg School of Public Health
SA22